We present two problems in multiple-robot motion planning that can be quitenaturally solved using techniques from the parallel processing community todictate how the robots interact with each other and techniques fromcomputational geometry to apply these techniques in the geometric environmentin which the robots operate. The first problem we consider is a load-balancingproblem in which a pool of work must be divided among a set ofprocessors in order to minimize the amount of time required to complete allthe work. We describe a simple polygon partitioning algorithm that allowstechniques from parallel processor scheduling to be applied in themultiple-robot setting in order to achieve a good balance of the work.The second problem is that of collision avoidance, where one must avoid thattwo (or more) processors occupy the same resource at the same time.For this problem, we describe a procedure for robot interaction that isderived from procedures used to shared-memory computers along with a geometricdata structure that can efficiently determine when there are potentialrobot collisions.